Energy-aware adaptive restricted access window for IEEE 802.11ah based smart grid networks

Wang, Yanru; Li, Yun; Chai, Kok Keong; Chen, Yue; Schormans, J.A.

doi:10.1109/smartgridcomm.2015.7436363

Cited by 27 publications

(15 citation statements)

References 7 publications

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“…Several mathematical RAW models have been proposed to calculate performance under specific network and traffic conditions. These models make use of different techniques, such as probability theory [3], Markov chains [4], [5], [6], [7], [8], and maximum likelihood estimation [8]. Some models assume stations have infinite packets to send (i.e., saturated model) [4], [6], [8], [7].…”

Section: Related Work On Ieee 80211ah Rawmentioning

confidence: 99%

“…A more accurate mathematical model was recently developed by Lyakhov et al [7], by taking into account the non-steady state of the backoff function at the beginning of the RAW. Wang et al [3] and Khorov et al [5], proposed an unsaturated model for low power IoT, assuming each station sends one packet per RAW slot interval. By taking into account the reset of the backoff state at the beginning of the RAW slot, Khorov et al presented a model to calculate the successful packet transmission probability for a certain RAW group duration [5], while Wang's model mainly focuses on energy consumption [3].…”

Section: Related Work On Ieee 80211ah Rawmentioning

confidence: 99%

“…Concretely, the model takes network conditions and a RAW configuration as input, and predicts one or more performance metrics (e.g., throughput or energy consumption) as output. Although several RAW models have been proposed in literature [3], [4], [5], [6], [7], [8], they are either too computationally hard to be used in real-time, or rely on certain simplifications (e.g., no capture effect, no communication errors, homogeneous stations, saturated traffic, etc.). Such simplifications limit the usage of the RAW models, making it difficult to apply them to realistic IoT networks.…”

Section: Introductionmentioning

confidence: 99%

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Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

Tian

López-Aguilera

García-Villegas

et al. 2019

IEEE Internet Things J.

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The new medium access method of IEEE 802.11ah, called Restricted Access Window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this paper, we present a surrogate model that can accurately predict RAW performance under a given Restricted Access Window (RAW) configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 10 17 possible data points. This is too big to achieve feasible training convergence. In this paper, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 10 5 data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks.Index Terms-IEEE 802.11ah, RAW, surrogate model, heterogeneous networks.

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Section: Related Work On Ieee 80211ah Rawmentioning

confidence: 99%

Section: Related Work On Ieee 80211ah Rawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

Tian

López-Aguilera

García-Villegas

et al. 2019

IEEE Internet Things J.

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“…To determine the optimal RAW parameters, several analytical models have been proposed to calculate RAW perfor-mance under specific network and traffic conditions. These models make use of different techniques, such as probability theory [5], Markov chains [4], [10], and maximum likelihood estimation [11]. Early works assume the network is operating under saturated state, where each station always has packets to send [10], [11].…”

Section: Related Workmentioning

confidence: 99%

“…In the past, several analytics models have been proposed to predict RAW performance [4], [5]. However, such models are too computationally hard to be used in real-time, and rely on simplifications and unrealistic assumptions (e.g., no capture effect, no hidden nodes, homogeneous stations, saturated or static traffic).…”

Section: Introductionmentioning

confidence: 99%

IEEE 802.11ah Restricted Access Window Surrogate Model for Real-Time Station Grouping

Tian

Mehari

Santi

et al. 2018

2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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The Restricted Access Window (RAW) mechanism proposed by IEEE 802.11ah promises to address one of the major problems of the Internet of Things (IoT): high channel contention in large-scale densely deployed sensor networks. The RAW feature allows the Access Point (AP) to divide stations into different groups, with only the stations in the same group being allowed to access the channel simultaneously. Existing station grouping strategies only support homogeneous scenarios, where all sensor stations have the same fixed data transmission interval, modulation and coding scheme (MCS) and packet size. In this paper, we present two contributions to address this issue. First, a surrogate model that predicts RAW performance given specific network conditions and RAW configuration parameters. It is fast to train and can be solved in real-time. Second, the Model-Based RAW Optimization Algorithm (MoROA), which uses the surrogate model to determine the optimal RAW configuration in real-time, for heterogeneous stations and dynamic traffic. We compare the accuracy of our surrogate model to simulation results. Performance of MoROA is compared to existing RAW optimization algorithms and traditional 802.11 channel access methods. The results shows that the trained surrogate model can accurately predict RAW performance with a relative error less than 7% and 10% for 95% and 98% of the RAW configurations respectively. MoROA achieves a throughput up to twice as high as traditional 802.11 channel access functions in dense heterogeneous networks.

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Throughput and Energy Efficiency Analysis of the IEEE 802.11ah Restricted Access Window Mechanism

Mahesh

Harigovindan

2018

Communications in Computer and Information Science

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Energy-aware adaptive restricted access window for IEEE 802.11ah based smart grid networks

Cited by 27 publications

References 7 publications

Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

IEEE 802.11ah Restricted Access Window Surrogate Model for Real-Time Station Grouping

Throughput and Energy Efficiency Analysis of the IEEE 802.11ah Restricted Access Window Mechanism

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